Learning DAGs without imposing acyclicity

Gherardo Varando

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arXiv · 2006.03005

Learning DAGs without imposing acyclicity

Gherardo Varando

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Abstract

We explore if it is possible to learn a directed acyclic graph (DAG) from data without imposing explicitly the acyclicity constraint. In particular, for Gaussian distributions, we frame structural learning as a sparse matrix factorization problem and we empirically show that solving an $\ell_1$-penalized optimization yields to good recovery of the true graph and, in general, to almost-DAG graphs. Moreover, this approach is computationally efficient and is not affected by the explosion of combinatorial complexity as in classical structural learning algorithms.

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BibTeX RIS

Gherardo Varando. 2020-06-04. Learning DAGs without imposing acyclicity. https://arxiv.org/abs/2006.03005

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